TY - JOUR
T1 - Production scheduling under demand uncertainty in the presence of feedback
T2 - Model comparisons, insights, and paradoxes
AU - Avadiappan, Venkatachalam
AU - Gupta, Dhruv
AU - Maravelias, Christos T.
N1 - Funding Information:
The authors acknowledge financial support from the National Science Foundation, USA under grant CBET-2026980. The authors acknowledge the simulations reported in this paper was performed using the Princeton Research Computing resources at Princeton University which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing.
Funding Information:
The authors acknowledge financial support from the National Science Foundation, USA under grant CBET-2026980 . The authors acknowledge the simulations reported in this paper was performed using the Princeton Research Computing resources at Princeton University which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12
Y1 - 2022/12
N2 - We investigate the importance of accounting for uncertainty a priori in production scheduling in the presence of feedback. First, we examine different optimization models (deterministic, robust, and stochastic programming), used to generate the open-loop schedules and describe the modeling of uncertainty in each case. Second, we present a formal procedure for carrying out closed-loop simulations in order to study and compare the closed-loop performance across the models as attributes such as the demand uncertainty observation horizon, order size max-mean relative difference, and load on the process network are varied. Finally, we analyze the results of the simulations to draw insights on how the above attributes affect the closed-loop performance of the different models across networks and expound on the paradoxes observed.
AB - We investigate the importance of accounting for uncertainty a priori in production scheduling in the presence of feedback. First, we examine different optimization models (deterministic, robust, and stochastic programming), used to generate the open-loop schedules and describe the modeling of uncertainty in each case. Second, we present a formal procedure for carrying out closed-loop simulations in order to study and compare the closed-loop performance across the models as attributes such as the demand uncertainty observation horizon, order size max-mean relative difference, and load on the process network are varied. Finally, we analyze the results of the simulations to draw insights on how the above attributes affect the closed-loop performance of the different models across networks and expound on the paradoxes observed.
KW - Mixed-integer programming
KW - Online scheduling
KW - Real-time optimization
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U2 - 10.1016/j.compchemeng.2022.108028
DO - 10.1016/j.compchemeng.2022.108028
M3 - Article
AN - SCOPUS:85141338254
SN - 0098-1354
VL - 168
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 108028
ER -